基于可变基函数和GentleAdaBoost的小波神经网络研究  

Research of wavelet neural network based on variable basis functions and GentleAdaBoost algorithm

作　　者：李翔[1] 朱全银[1] 王尊[2] 

机构地区：[1]淮阴工学院计算机工程学院,江苏淮安223003 [2]南京理工大学电子工程与光电技术学院,江苏南京210094

出　　处：《山东大学学报（工学版）》2013年第5期31-38,共8页Journal of Shandong University（Engineering Science）

基　　金：国家星火计划资助项目(2011GA690190);江苏省属高校自然科学重大基础研究资助项目(11KJA460001)

摘　　要：针对传统小波神经网络(wavelet neural network,WNN)受隐含层节点数影响大、网络误差易陷入局部极小、预测结果不稳定的问题,提出使用GentleAdaBoost和小波神经网络相结合的方法,提高网络预测精度和泛化能力。该方法首先对样本数据进行预处理并初始化测试数据分布权值;然后通过选取不同的隐含层节点数、小波基函数构造出不同类型的小波神经网络弱预测器序列并对样本数据进行反复训练;最后使用GentleAdaBoost算法将得到的多个小波神经网络弱预测器组成新的强预测器并进行回归预测。对UCI数据库中数据集进行仿真实验,结果表明,本方法比传统小波神经网络预测平均误差减少40%以上,有效地提高了神经网络预测精度,为小波神经网络应用提供借鉴。In view that the traditional wavelet neural network （WNN） was affected largely by the number of hidden lay er nodes, easy to fall into local minimum and had unstable forecast results, a method of combining the GentleAdaBoost algorithm with WNN was put forward to improve the forecasting accuracy and generalization ability. First, this method performed the pretreatment for the historical data and initialized the distribution weights of test data. Second, different hidden layer nodes and wavelet basis functions were selected randomly to construct weak predictors of WNN and trained the sample data repeatedly. Finally, the multiple weak predictors of WNN were used to form a new strong predictor by GentleAdaBoost algorithm for regression forecasting. A simulation experiment using datasets from the UCI database was carried out. The results showed that this method had reduced the average error value by more than 40% compared to the traditional WNN, improved the forecasting accuracy of neural network, and could provide references for the WNN fore casting.

关 键 词：小波神经网络 基函数 迭代算法 GentleAdaBoost算法 强预测器 回归预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]